With the spread of electronic health records, increasingly large data repositories of clinical and other patient derived data are being built. These databases are large and difficult for any one specialist to analyze. To find the hidden associations within such data, we review methods for large-scale data-mining on electronic medical records, methods in natural language processing and text-mining of medical records, methods for using ontologies for tagging of unstructured clinical notes.

SCHEDULE: TUE, THU 2:15 PM - 3:30 PM
LOCATION: Huang Engineerig Center 18 for Fall 2013
CREDITS: 3
Discussion Section: Medical School Office Building, X-271, Wed, 2:15 - 3:05
Office hours:

• Wed 3:05 - 4:05 pm (after discussion section)
• Thursday 3:30 - 4:30 PM (after Thur lecture)

TAs: Bethany Percha (blpercha AT gmail.com) and Michael Januszyk (januszyk AT stanford.edu)

The course has four modules. The first module will review the medical data miner’s tool-kit—including the use of ontologies for data-mining. The remaining modules will review three problem areas and computational methods used in that problem area via a set of 3-5 lectures ending in a “mini project” as home work. Each module will cover a new application area (e.g. drug safety surveillance, clinical text mining) and a new method (e.g. association rules, logistic regression). The course will use real, de-identified, large size patient datasets (millions of patients range) that are made available for home work projects associated with the course. This course is also offered in a 2 credit version (BIOMEDIN 225) which meets at the same time but requires only one home work.

Course Flyer

All homework assignments will be due 3 weeks after they are distributed, before the start of lecture.

• Go to https://coursework.stanford.edu/ and join BIOMEDIN 215
  • Coursework Site
• R-tutorial

Previous Years

• 2011

Machine learning: an algorithmic perspective, Stephen Marsland
http://www.amazon.com/Machine-Learning-Algorithmic-Perspective-Recognition/dp/1420067184

Introduction to the practice of statistics, David S. Moore, George P. McCabe
http://searchworks.stanford.edu/view/5470778
http://www.amazon.com/Introduction-Practice-Statistics-George-McCabe/dp/071676282X

The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Trevor Hastie, Robert Tibshirani and Jerome Friedman
http://www-stat.stanford.edu/~tibs/ElemStatLearn/

Mining of Massive Datasets, Anand Rajaraman and Jeff Ullman
http://infolab.stanford.edu/~ullman/mmds.html

The Petabyte Age Because More Isn't Just More — More Is Different
The Unreasonable Effectiveness of Data
A few useful things to know about machine learning ← only works for on campus access. Same content in http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf